Waqar Ali1, 2, Salah Ud Din1, Abdullah Aman Khan1, Saifullah Tumrani1, Xiaochen Wang1, Jie Shao1, 3, *
CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 1065-1078, 2020, DOI:10.32604/cmc.2020.010017
Abstract Recommender systems are rapidly transforming the digital world into
intelligent information hubs. The valuable context information associated with the users’
prior transactions has played a vital role in determining the user preferences for items or
rating prediction. It has been a hot research topic in collaborative filtering-based
recommender systems for the last two decades. This paper presents a novel Context
Based Rating Prediction (CBRP) model with a unique similarity scoring estimation
method. The proposed algorithm computes a context score for each candidate user to
construct a similarity pool for the given subject user-item pair and intuitively choose the
highly influential… More >
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